khadije fattahi dolatabadi; hosin babazadeh; payam najafi; hossin sedghi
Abstract
To prevent water stress in plants and have sustainable water management in the field, fast and accurate determination of irrigation time is one of the most important issues. Measuring soil moisture and leaf surface temperature are two methods of determining time of irrigation. In this research, by combination ...
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To prevent water stress in plants and have sustainable water management in the field, fast and accurate determination of irrigation time is one of the most important issues. Measuring soil moisture and leaf surface temperature are two methods of determining time of irrigation. In this research, by combination of these two methods, a model for planning and management of forage maize irrigation (cultivar SC-701) is presented. The air relative humidity (RH) and temperature (Ta), leaf surface temperature (TL), and soil moisture content (SM) were measured in 2013 and, by using artificial neural network model and multiple stepwise method, a regression model was developed. Experiments were carried out in 2014 with five treatments including 100%, 85%, 75%, 65%, and 35% total available water (TAW), with four replications, Irrigation was carried out when soil moisture content reached the treatments moisture level. Measurements of the previous year were repeated and the model was calibrated. The results of the first year showed a correlation (R2=0.87) between the parameters RH, Ta, TL, Ta-TL as independent variable and SM as the dependent variable. Then, using three input parameters of air temperature, leaf surface temperature, and relative humidity, Determination Coefficient of soil moisture content model was calculated as R2= 0.92. In this model, soil moisture has an inverse relation with (Ta) and (TL-Ta) variables, but a direct relation with RH. Soil moisture content was compared using the model for the second year treatments and compared with the measured values. The difference in soil moisture content measured and estimated by the model at the peak solar radiation time (at noon) was less than ±10%. The model estimated 75% TAW treatment data well, with very small difference compared to the measured value.
NOZAR GHAHREMAN; Azar Sahragard
Abstract
Leaf Wetness Duration (LWD) is a key element in plant water balance. Water wets leaf surfaces following various events such as rainfall, dew formation, etc. The result of the interaction between atmosphere and the plant leaf and canopy characteristics determine the leaves wetness duration (LWD) on the ...
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Leaf Wetness Duration (LWD) is a key element in plant water balance. Water wets leaf surfaces following various events such as rainfall, dew formation, etc. The result of the interaction between atmosphere and the plant leaf and canopy characteristics determine the leaves wetness duration (LWD) on the plant. This variable is measured by electronic sensors, but, due to the difficulty of measurement, various empirical models using meteorological data have been developed for its estimation. Despite their limitations, these models are widely used. In this study, two empirical models of LWD estimation using relative humidity were evaluated at Paliz station in Fars province. The simplest empirical model uses only the relative humidity (RH), and wetness occurs when the RH is greater than a certain threshold. On the basis of different studies under wet conditions, for several plants, a threshold value of 87% RH has been determined for this purpose. In this study, the optimized RH-threshold model did a better job compared to non-optimized constant threshold and the extended threshold models in both warm and cold seasons.